SDE III

Role Overview

You will own the end-to-end Backend Engineering lifecycle for our AI-powered medical coding platform. This role focuses on implementing an Agentic SDLC, where you design microservices in Java/Spring Boot and integrate AI agents to accelerate development while maintaining rigorous quality standards.

Key Responsibilities:

• Backend & Architecture: Design scalable Java Spring Boot services using a microservices architecture. Own server-side logic, data models, APIs, and integrations with full accountability for performance and robustness.
• Agentic SDLC Ownership: Drive the entire lifecycle of AI agent integration: spec/design (HLD/LLD), implementation, code review gating, testing generation, and production monitoring. Build scaffolding including repo-level context files, custom subagents, slash commands, and MCP integrations.
• Quality & Evaluation: Define rigorous standards for reviewing agent-generated code versus human-written logic. Instrument workflows to measure cycle time, escape defect rates, and test coverage on AI-authored changes; iterate based on data rather than intuition.
• Mentorship & Leadership: Lead cross-functional design reviews, onboard engineers onto new AI workflows, run enablement sessions, and set guardrails for security, licensing, and data handling.

Requirements (Must-Haves):

• Experience: 6+ years in backend engineering with at least 2 years as a tech lead or principal engineer.
• Tech Stack: Deep expertise in Java, Spring Boot, Hibernate/JPA, and Microservices architecture.
• AI Integration (Critical): Proven experience shipping production software where AI agents were primary tools within the last 12 months. Must have used tools like Cursor, Copilot Workspace, Aider, or Claude Code for design, implementation, code review, testing, and debugging on large codebases.
• Judgment: Practical ability to manage context limits in LLMs, identify hallucinated APIs, mitigate silent test failures, and prevent agent-induced security risks.
• Fundamentals: Strong Git/CI/CD skills adapted for high volumes of AI-authored code; solid understanding of SDLC across multiple teams.

Nice-to-Have Skills:

• Built custom agents, subagents, or MCP servers connecting to internal systems like Jira and BigQuery.
• Expertise in prompt engineering at the repository scale (e.g., CLAUDE.md style instruction files) and codebase indexing strategies.
• Familiarity with LLM evaluation pipelines, regression harnesses, or accuracy testing frameworks.
• Experience using Observability tools like New Relic or Datadog for agent-assisted incident triage.
• Domain knowledge in Healthcare (FHIR/HL7) and Medical Coding workflows.